School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA.
School of Public Health, Curtin University, Perth, WA 6845, Australia.
Nutrients. 2019 Apr 18;11(4):877. doi: 10.3390/nu11040877.
Obtaining accurate food portion estimation automatically is challenging since the processes of food preparation and consumption impose large variations on food shapes and appearances. The aim of this paper was to estimate the food energy numeric value from eating occasion images captured using the mobile food record. To model the characteristics of food energy distribution in an eating scene, a new concept of "food energy distribution" was introduced. The mapping of a food image to its energy distribution was learned using Generative Adversarial Network (GAN) architecture. Food energy was estimated from the image based on the energy distribution image predicted by GAN. The proposed method was validated on a set of food images collected from a 7-day dietary study among 45 community-dwelling men and women between 21-65 years. The ground truth food energy was obtained from pre-weighed foods provided to the participants. The predicted food energy values using our end-to-end energy estimation system was compared to the ground truth food energy values. The average error in the estimated energy was 209 kcal per eating occasion. These results show promise for improving accuracy of image-based dietary assessment.
自动获取准确的食物份量估计具有挑战性，因为食物准备和食用过程会导致食物形状和外观发生很大变化。本文旨在通过使用移动食物记录拍摄的进食图像来估算食物的能量数值。为了模拟进食场景中食物能量分布的特征，引入了一个新的概念“食物能量分布”。使用生成对抗网络（GAN）架构学习将食物图像映射到其能量分布。基于 GAN 预测的能量分布图像，从图像中估算食物能量。该方法在一组从 21-65 岁的 45 名社区居住的男性和女性为期 7 天的饮食研究中收集的食物图像上进行了验证。食物能量的真实值是通过提供给参与者的预先称重的食物获得的。我们的端到端能量估计系统预测的食物能量值与食物能量的真实值进行了比较。每个进食事件的估计能量的平均误差为 209 千卡。这些结果表明，基于图像的饮食评估的准确性可以得到改善。
